Characterization and risk assessment of arsenic contamination in soil–plant (vegetable) system and its mitigation through water harvesting and organic amendment

Bhattacharyya, Kallol; Sengupta, Sudip; Pari, Arnab; Halder, Sanjay; Bhattacharya, P.; Pandian, B. J.; Chinchmalatpure, Anil R.

doi:10.1007/s10653-020-00796-9

Cited by 21 publications

(8 citation statements)

References 49 publications

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“…Mandal et al (2019b) found similar cases of reduction in As in wheat and maize grain with sugarcane bagasse > paddy husk > rice straw > vermicompost > FYM. Organic treatments were also effective in lowering the amount of As in cauliflower, spinach and tomato in the trend of vermicompost > mustard cake > FYM (Bhattacharyya et al, 2021). In all cases, a possible organo-As complexation in soil was propounded as the underlying reason for reduced uptake in plant edibles.…”

Section: Arsenic Accumulation In Ricementioning

confidence: 96%

“…In comparison to the certified value of 290 ± 30 μg kg TCR assumes great significance in dietary risk assessment as it categorizes the lifetime exposure of carcinogenic As for human individual. The TCR calculation is based on the following equation (Antoine et al, 2017;Bhattacharyya et al, 2021): BWa= the body weight of 68 kg ATc= the averaged carcinogenic exposure time (365days*70yrs) 10 −3 = the unit conversion factor (Antoine et al, 2017).…”

Section: Instrumental Analysismentioning

confidence: 99%

“…The results are highly variable unless properly adopted (Carrijo et al, 2018). Organic amendments, on the other hand, reduce As bioavailability in soils through organo-As chelation, and thus in plants, as previously stated for sesame , wheat, and maize (Mandal et al, 2019b), and vegetables (Bhattacharyya et al, 2021). Since there are currently no research on the effectiveness of organic amendments in the rice environment, we decided to conduct a study that combined irrigation and organic management.…”

Section: Introductionmentioning

confidence: 99%

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Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: Introducing machine learning-based prediction models from field data

Sengupta

Bhattacharyya

Mandal

et al. 2021

Agriculture, Ecosystems & Environment

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Section: Arsenic Accumulation In Ricementioning

confidence: 96%

Section: Instrumental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: Introducing machine learning-based prediction models from field data

Sengupta

Bhattacharyya

Mandal

et al. 2021

Agriculture, Ecosystems & Environment

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“…The World Health Organization (WHO) has established a guideline value of 10 μg L −1 for As in drinking water. Although contaminated irrigation water also contributes to As exposure by enhancing As concentrations in food crops (Mandal et al, 2019;Bhattacharyya et al, 2021), no WHO or international guideline value for irrigation water has been established to date.…”

Section: Introductionmentioning

confidence: 99%

Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil

Mandal

Sengupta

Sarkar

et al. 2021

Front. Environ. Sci.

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It is now well-established that not just drinking water, but irrigation water contaminated with arsenic (As) is an important source of human As exposure through water-soil-rice transfer. While drinking water As has a permissible, or guideline value, quantification of guideline values for soil and irrigation water is limited. Using published data from 26 field studies (not pot-based experiments) from Asia, each of which reported irrigation water, soil and rice grain As concentrations from the same site, this meta-analysis quantitatively evaluated the relationship between soil and irrigation water As concentrations and the As concentration in the rice grain. A generalized linear regression model revealed As in soil to be a stronger predictor of As in rice than As in irrigation water (beta of 16.72 and 0.6, respectively, p < 0.01). Based on the better performing decision tree model, using soil and irrigation water As as independent variables we determined that Asian paddy soil As concentrations greater than 14 mg kg−1 may result in rice grains exceeding the Codex recommended maximum allowable inorganic As (i-As) concentrations of 0.2 mg kg−1 for polished rice and 0.35 mg kg−1 for husked rice. Both logistic regression and decision tree models, identified soil As as the main determining factor and irrigation water to be a non-significant factor, preventing determination of any guideline value for irrigation water. The seemingly non-significant contribution of irrigation water in predicting grain i-As concentrations below or above the Codex recommendation may be due to the complexity in the relationship between irrigation water As and rice grains. Despite modeling limitations and heterogeneity in meta-data, our findings can inform the maximum permissible As concentrations in Asian paddy soil.

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“…In accordance with the guidance provided by the Swedish national food agency, an effective tool for determination of risk characterization by risk thermometer were developed, as detailed by Sengupta et al (2021). These risk thermometers, based on the methodology outlined by Chowdhury et al (2020), enable us to anticipate HMs exposure in food, considering healthbased tolerable daily intake (TDI) as their basis, while also factoring in the critical target cancer risk (TCR) calculated using the formula by Bhattacharyya et al (2021) in dietary risk assessment.…”

Section: Risk Assessment Through Fiam Modelmentioning

confidence: 99%

Assessment of risk perception by toxic metals in calcareous soil: evaluate environmental and health fate using multimodal statistical approach

Mondal,

Ghosh,

Banerjee

et al. 2023

Preprint

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Faulty irrigation practices and industrial activities lead to the pollution of heavy metals (HMs) in agricultural soil, resulting in adverse effects on human health. The present study was conducted for the assessment of source identification, probabilistic health hazard assessments, and analysis of dietary risks associated with HM pollution in the calcareous soil of India. The mean concentrations of total and bioavailable forms of heavy metals (Cr, Ni, Cd, Pb, Cu) surpassed the permissible limits in sample areas (zone1 and 2), with zone 1 showing higher contamination levels compared to zone 2. The free ion activity model (FIAM) was employed to detect HMs in contaminated soil and assess their potential transfer to rice grains. Hazard quotient values were notably higher than the safe threshold (FIAM-HQ < 0.5) for Cr (2.87E-01), Ni (1.08E-01), and Pb (1.88E-01), except for Cd (1.49E-02) and Cu (1.27E-03), which remained within safe limits. Risk thermometer indicates high and moderate human health risk for Cr (CrSAMOE= 0.006) and Ni (NiSAMOE= 0.03), respectively. Self-organizing map (SOM) and positive matrix factorization (PMF) identify pollution sources in the calcareous region. Monte Carlo simulation (MCS) unveiled that children were more vulnerable to total carcinogenic risk (TCR) compared to adults through the ingestion pathway. A geostatistical approach was employed to predict the spatial distribution patterns of various heavy metals across the area. This comprehensive evaluation, utilizing appropriate and reasonable methods, serves as a valuable resource for environmental scientists and policymakers aiming to manage and mitigate HM pollution in agricultural soils near residential areas.

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Characterization and risk assessment of arsenic contamination in soil–plant (vegetable) system and its mitigation through water harvesting and organic amendment

Cited by 21 publications

References 49 publications

Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: Introducing machine learning-based prediction models from field data

Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: Introducing machine learning-based prediction models from field data

Meta-Analysis Enables Prediction of the Maximum Permissible Arsenic Concentration in Asian Paddy Soil

Assessment of risk perception by toxic metals in calcareous soil: evaluate environmental and health fate using multimodal statistical approach

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